TY - GEN
T1 - Don't look stupid
T2 - 20th Anniversary ACM Conference on Computer Supported Cooperative Work, CSCW 2006
AU - McNee, Sean M.
AU - Kapoor, Nishikant
AU - Konstan, Joseph A.
PY - 2006
Y1 - 2006
N2 - If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.
AB - If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.
KW - Collaborative filtering
KW - Content-based filtering
KW - Digital libraries
KW - Human-recommender interaction
KW - Information seeking
KW - Personalization
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=34547205105&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547205105&partnerID=8YFLogxK
U2 - 10.1145/1180875.1180903
DO - 10.1145/1180875.1180903
M3 - Conference contribution
AN - SCOPUS:34547205105
SN - 1595932496
SN - 9781595932495
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 171
EP - 180
BT - Proceedings of the 20th Anniversary ACM Conference on Computer Supported Cooperative Work, CSCW 2006
Y2 - 4 November 2006 through 8 November 2006
ER -